Detection of lane marking using visual information is a primary requirement for supporting driver assistance and safety features such as Lane Departure Warning (LDW), Lane Keep Assistance (LKA) and Forward Collision Alert (FCA). For detecting lane markings, cameras with clear sensors or color filtered sensors are used. For driver assistance applications, a vision sensor, i.e., camera is mounted in front of the vehicle to capture the road images. The camera is an assembly of a CMOS/CCD sensor of size VGA or HD used along with a lens of horizontal field of view (FOV) around 45 degree and vertical field of view of around 30 degree. The lane markings may vary in color (white and yellow for India, USA, Europe; Blue for South Korea), width and shape (solid and dashed).
Various conventional techniques to detect lanes include edge detection and line approximations to the edges and using global segmentation techniques. For example, in one technique the image region is the divided into sub regions and Hough lines are fitted to the edges in each sub regions to determine vanishing point. Another technique includes inverse perspective mapping of the image, detecting the lane marks by thresholding and then fitting Hough lines. Some techniques recommended using thresholding methods to extract the road lane markings.
The inventors here have recognized several technical problems with such conventional systems, as explained below. Although the aforementioned techniques perform well in the near view of the road and straight lane markings, however, to support the practical scenarios of lane detection, improved detection distance and detection of shapes like curvy and dashed lane markings should be addressed. Additionally, lane detection techniques can be improved from the accuracy and performance point of view for the embedded platforms used in vehicles.